网约车平台算法个性化定价、乘客履约率及其监管对策  

Algorithmic Personalized Pricing,Passengers’Order Compliance Rates and Regulatory Countermeasures for Online Taxi-Hailing Platforms

作　　者：冯苏苇[1,2] 林昌 FENG Suwei;LIN Chang(School of Public Economics and Administration,Shanghai University of Finance and Economics,Shanghai 200433,China;Research Center for Transport Economics and Policy,Shanghai University of Finance and Economics,Shanghai 200433,China)

机构地区：[1]上海财经大学公共经济与管理学院,上海200433 [2]上海财经大学交通经济与政策研究中心,上海200433

出　　处：《华南理工大学学报（自然科学版）》2024年第2期104-112,共9页Journal of South China University of Technology(Natural Science Edition)

基　　金：国家自然科学基金资助项目(71871131)。

摘　　要：网约车平台算法个性化定价产生了复杂的市场影响,相比传统出租车服务,网约车乘客违约率达30%左右,因此算法个性化定价对乘客违约率的影响机制以及乘客是否履约的关键特征值得探索。文中尝试运用矩形Hotelling模型建立算法个性化定价与乘客违约率的因果关联机制,以两个网约车平台之间的Stackelberg博弈模型揭示歧视性定价、乘客违约率与平台之间竞争强度的关系。进一步运用网约车平台订单大数据,以Bhattacharyya距离、提升决策树及改进拉斯维加斯方法(包裹法)等归纳学习工具对网约车平台百万量级订单进行数据挖掘,找出决定乘客是否履约的关键特征。分析结果表明,平台进行个性化定价时乘客的最终消费选择主要取决于价格因素;而改进车辆匹配、派单策略及减少乘客候车时间可显著提高订单履约率。研究结果对网约车平台完善定价及运营策略以维持双边市场用户数量、保证平台持续成功运营具有重要参考价值,也为反垄断部门干预平台个性化定价提供了理论依据。The algorithmic personalized pricing of online taxi-hailing platforms has produced complex market impacts,and compared with traditional cab service,the order cancellation rate of online taxi passengers reaches about 30%.Therefore,it is worth exploring the impact mechanism of algorithmic personalized pricing on passenger cancellation rates and the key characteristics of whether passengers fulfill their orders.This paper tried to establish the causal mechanism between algorithmic personalized pricing and passengers’order cancellation rate using rectangular Hotelling model.Using a Stackelberg game model between two taxi-hailing platforms,it revealed the relationship among discriminatory pricing,passenger cancellation rate,and competition intensity between two platforms.Furthermore,based on the big data of online taxi-hailing platform orders,this paper applied some inductive learning tools such as Bhattacharyya distance,Gradient Boosting Decision Tree(GBDT)and improved Las Vegas method for wrapper-method feature selection to data mining of millions of orders on online taxi-hailing platforms to find out the key features that determine whether passengers take the orders or not.Analysis results show that the final consumption choice of passengers mainly depends on the price factors.And improving the match and dispatch strategies to reduce passengers’waiting time can significantly improve fulfillment rate.The results are helpful for taxi-hailing platform to appropriately design the pricing and operation strategies to maintain the number of customers in the two-sided markets,which ensures the sustainable and successful operation of the platform.Meanwhile,it will provide a theoretical basis for antitrust authorities to intervene in platform personalized pricing.

关 键 词：网约车 算法个性化定价 特征工程 履约率 监管 

分 类 号：F570.5[经济管理—产业经济] TP39[自动化与计算机技术—计算机应用技术]